Abstract.The major challenge when integrating information systems in any domain such as e-Government is the challenge of Interoperability. One can distinguish between three aspects of Interoperability; technical, semantic, and organizational. The technical aspect has been widely tackled especially after the ubiquity of internet technologies. The semantic and organizational aspects deal with sharing the same understanding (semantics) of exchanged information among all applications and services, in addition to modeling and re-engineering governmental processes to facilitate process cooperation that provision seamless e-government services. In this paper, we present the case of the Palestinian Interoperability Framework ‘Zinnar’, which is a use case of using ontology in e-government (i.e., data and process governance) to tackle the issues of semantic and organizational interoperability. The followed methodology resulted in a success story within a very short time and has produced a framework that is intuitive, elegant, and easy to understand and implement.

Abstract.This paper is motivated by the massively increasing structured data on the Web (Data Web), and the need for novel methods to exploit these data to their full potential. Building on the remarkable success of Web 2.0 mashups, this paper regards the internet as a database, where each web data source is seen as a table, and a mashup is seen as a query over these sources. We propose a data mashup language, which allows people to intuitively query and mash up structured and linked data on the web. Unlike existing query methods, the novelty of MashQL is that it allows people to navigate, query, and mash up a data source(s) without any prior knowledge about its schema, vocabulary, or technical details. We even do not assume even that a data source should an online or inline schema. Furthermore, MashQL supports query pipes as a built-in concept, rather than only a visualization of links between modules.

Abstract.This article is motivated by the importance of building web data mashups. Building on the remarkable success of Web 2.0 mashups, and specially Yahoo Pipes, we generalize the idea of mashups and regard the Internet as a database. Each internet data source is seen as a table, and a mashup is seen as a query on these tables. We assume that web data sources are represented in RDF, and SPARQL is the query language. We propose a query-by-diagram language called MashQL. The goal is to allow people to build data mashups diagrammatically. In the background, MashQL queries are translated into and executed as SPARQL queries. The novelty of MashQL is that it allows querying a data source without any prior understanding of the schema or the structure of this source. Users also do not need any knowledge about RDF/SPARQL to get started.

Abstract.This chapter describes various graphical notations for rule modeling. Rule modeling methodologies, empowered with graphical notations, play an important role in helping business experts and rule engineers to represent business rules formally for further deployment into a rule execution system. Rules, represented graphically, can be easier understood by business people and by technicians without intensive technical learning. In this chapter we mainly focus on three graphical notations for rules: UML/OCL, URML and ORM. UML/OCL is a mainstream modeling technology in software development, which is also accommodated by some business experts when modeling a system at the semi-formal, platform independent level. URML extends UML with additional graphical symbols and the concept of a rule, which allows visualization of different rule types on top of UML class diagrams. ORM is an alternative methodology with a rich graphical notation for modeling a domain at the conceptual level. The methodological power, graphical expressivity, and verbalization capabilities of ORM have made it the most popular language within the business rules community. This chapter introduces each of these graphical notations, explain how it can be used, and compare them against each other.

Abstract. This chapter presents an ontology for customer complaint management, which has been developed in the CCFORM project. CCFORM is an EU funded project (IST-2001-38248) with an aim of studying the foundation of a central European customer complaint portal. The idea is that any consumer can register a complaint against any party about any problem, at one portal. This portal should: support 11 languages, be sensitive to cross-border business regulations, dynamic, and can be extended by companies. To manage this dynamicity and to control companies' extensions, a customer complaint ontology (CContology) has to be built to underpin the CC portal. In other words, the complaint forms are generated based on the ontology. The CContology comprises classifications of complaint problems, complaint resolutions, complainant, complaint-recipient, ''best-practices'', rules of complaint, etc. The main uses of this ontology are 1) to enable consistent implementation (and interoperation) of all software complaint management mechanisms based on a shared background vocabulary, which can be used by many stakeholders. 2) to play the role of a domain ontology that encompasses the core complaining elements and that can be extended by either individual or groups of firms; and 3) to generate CC-forms based on its ontological commitments and to enforce the validity (and/or integrity) of their population. To end, we outline our experience in applying the methodological principles (Double-Articulation and Modularization) and the tool (DogmaModeler) that we used in developing the CContology.Keywords: e-Commerce, CRM, Customer Relationship management, Customer Complaints Forms, Ontology, Customer Complaint Ontology, Semantics, Domain Axiomatization, Multilingual Ontology, Ontology Engineering, Methodology, Double Articulation, Modularization Context, Gloss, Lexon, DogmaModeler.

Abstract. The goal of this article is to formalize Object Role Modeling (ORM) using the DLR description logic. This would enable automated reasoning on the formal properties of ORM diagrams, such as detecting constraint contradictions and implications. In addition, the expressive, methodological, and graphical capabilities of ORM make it a good candidate for use as a graphical notation for most description logic languages. In this way, industrial experts who are not IT savvy will still be able to build and view axiomatized theories (such as ontologies, business rules, etc.) without needing to know the logic or reasoning foundations underpinning them. Our formalization in this paper is structured as 29 formalization rules, that map all ORM primitives and constraints into DLR, and 2 exceptions of complex cases. To this end, we illustrate the implementation of our formalization as an extension to DogmaModeler, which automatically maps ORM into DIG and uses Racer as a background reasoning engine to reason about ORM diagram.